Emerging Statistical Models for the Analysis of Genomic Data

In this paper we review important emerging statistical models that have been recently developed and used for genomic data analysis. First, we summarize general background and some critical issues in genomic data mining. We then describe a novel concept of statistical significance, so-called false discovery rate, the rate of false positives among all positive findings, which has been suggested to control the error rate of numerous false positives in large screening biological data analysis. In the next section two recent statistical testing methods---significance analysis of microarray (SAM) and local pooled error (LPE) tests are introduced. We next introduce statistical modeling in genomic data analysis such as ANOVA and heterogeneous error modeling (HEM) approaches that have been suggested for analyzing microarray data obtained from multiple experimental and/or biological conditions. ™ gene expression, Misclassification penalized posterior (MiPP), Significance analysis of microarray (SAM), Supervised learning, Unsupervised learning